Slides

Report
The Structure of Networks
with emphasis on information and social networks
T-214-SINE
Summer 2011
Review
Ýmir Vigfússon
Part I

Chapters 2, 3 and 5
Graph Theory

Graph
◦ Nodes connected by edges

Path
◦ Set of nodes where each consecutive pair is
joined by an edge

Component
◦ Set of nodes where each two nodes are
connected by some path

Distance
◦ Length of shortest path between two nodes

Breadth-first search (BFS)
◦ Method to find shortest paths from one node to
all others
Breadth-first search (BFS)

From the given node (root)
◦ Find all nodes that are directly connected
 These are labeled as “distance 1“
◦ Find all nodes that are directly connected to
nodes at distance 1
 If these nodes are not at distance 1, we label them
as “distance 2“
◦ ...
◦ Find all nodes that are directly connected to
nodes at distance j
 If these nodes are not already of distance at most
j+1, we label them as „distance j+1“
Breadth-first search (BFS)
Breadth-first search (BFS)
Strong and Weak Ties

Triadic closure
◦ If two people have friends in common, they‘re
more likely to become friends themselves

Clustering coefficient
◦ Over every pair of my friends, what fraction
them are friends themselves?

Local bridge
◦ If A and B are friends and have no other
friends in common
Strong and Weak Ties
Suppose edges are now either strong
(friends) or weak (acquaintances)
 Strong Triadic Closure Property
(STCP)

◦ For every node A with strong ties to some
friends B and C, then B and C are connected
by an edge (either weak or strong).

Thm:
◦ If a node A satisfies STCP and is involved in at
least two strong ties, then any local bridge it is
involved in must be a weak tie
Strong and Weak Ties
People seem to maintain a small number
(~40) of strong ties (Facebook/Twitter)
 Structural hole:

◦ The „empty space“ or „missing“ edges
between groups that don‘t interact much

Betweenness of an edge:
◦ Essentially the number of shortest paths
(between pairs of nodes) that use the edge
 But if there are multiple shortest paths between A
and B, say k of them, the contribution to
betweenness of intermediate edges is only 1/k
instead of 1
Positive and Negative Relationships
Suppose all edges exist, and are either +
(friends) or – (enemies)
 (Strong) Structural Balance:

◦ For every set of three nodes, either three +
edges or exactly one + edge.

Balance Theorem:
◦ In a balanced complete graph, either all nodes
are friends or it can be divided into two
coalitions of mutual friends such that people
in different groups are enemies
Positive and Negative Relationships

Weak Structural Balance:
◦ No set of three nodes have two + edges and
one - edge.

Weak Balance Theorem:
◦ In a weakly balanced complete graph, nodes
can be divided into groups of mutual friends
such that people from different groups are
enemies
Positive and Negative Relationships

What if not all the edges exist?

Thm:
◦ A signed graph is strongly balanced if and only
if it contains no cycle with an odd number of
negative edges
Part II

Chapters 6, 7, 8, 9
Game Theory

Game theory deals with the outcome of
person‘s decisions in various situations
◦ How to choose among different options
◦ People‘s choices when interacting with others

Def: A game consists of three things.
◦ (1) A set of players
◦ (2) Each player has set of options how to
behave called strategies
◦ (3) For each choice of strategies, each player
receives a payoff from the game.
Game Theory
Your partner
You
Presentation
Exam
Presentation
90,90
86,92
Exam
92,86
88,88
Strategies

Payoff for
row player,column player
Prisoner‘s dilemma
◦ Each player thinks studying for exam is safer,
but this doesn‘t reach the social optimum
Game Theory
How are the players likely to behave?
 Assumptions

◦ The payoff for a player should capture all
rewards
 Including altruism
◦ Players know all possible strategies and
payoffs to other players
◦ Players are rational
 Each players wants to maximize her own payoff
 Each player succeeds in picking the optimal strategy
Game Theory

Best response
◦ The best choice of one player, given a belief of
what the other player will do
 E.g. „U is a best response to L“

Dominant strategy
◦ If a strategy is the best response to every
strategy of the other player
◦ We can assume it will be played!
Game Theory

Nash equilibrium
◦ For strategy S by player 1 and T by player 2,
the pair (S,T) is a Nash equilibrium if S is a best
response to T, and T is a best response to S
◦ More generally, a cell in the payoff matrix is a
Nash equilibrium if no player wants to
unilaterally (i.e. by himself) deviate to an
alternative strategy

A game might have multiple Nash
equilibria, or none.
Important Games

Battle of the sexes
◦ Which kind of movie to rent?
Romance
Thriller
Romance
Thriller
1, 2
0, 0
0, 0
2, 1
◦ Two equilibria, but which one will be played?

Hard to predict the outcome
◦ Depends on social conventions
Important Games

Stag Hunt
◦ If hunters work together, they can
catch a stag
◦ On their own they can each catch a hare
◦ If one hunter tries for a stag, he gets nothing
Hunt Stag
Hunt Hare

Hunt Stag
Hunt Hare
4, 4
3, 0
0, 3
3, 3
Two equilibria, but “riskier“ to hunt stag
◦ What if other player hunts hare? Get nothing
◦ Similar to prisoner‘s dilemma
 Must trust other person to get best outcome!
Game Theory

Mixed strategies
◦ Each player now commits to a strategy with a
certain probability
 e.g. Player 1 has probability p of choosing strategy H

Nash‘s Theorem:
◦ When we allow mixed strategies, Nash
equilibrium always exists.

Social optimum
◦ The cell in the payoff matrix that maximizes
the sum of the players‘ payoffs.
Mixed Strategies: Example

Penalty-kick game
◦ Soccer penalties have been studied extensively
Left
Defend left
Defend right
0.58, -0.58
0.95, -0.95
Right
0.93, -0.93
0.70, -0.70
◦ Suppose goalie defends left with probability q
◦ Kicker indifferent when
 (0.58)(q) + (0.95) (1-q) = (0.93)(q) + (0.70) (1-q)
◦ Get q =0.42. Similarly p=0.39
◦ True values from data? q=0.42 , p=0.40 !!
 The theory predicts reality very well
Evolutionary Game Theory
Game theory continues to apply even if
no individual is overtly reasoning or
making explicit decisions
 Natural selection

◦ More fit organisms will produce more
offspring
◦ This causes genes that provide greater fitness
to increase their representation in the
population

But how do we measure „fitness“?
Evolutionary Game Theory

Key insight
◦ Many behaviors involve the interaction of multiple
organisms in a population
◦ The success of an organism depends on how its
behavior interacts with that of others
 Can‘t measure fitness of an individual organism
◦ So fitness must be evaluated in the context of the
full population in which it lives

Analogous to game theory
◦ Organisms‘s genetically determined
characteristics and behavior = Strategy
◦ Fitness = Payoff
◦ Payoff depends on strategies of organisms with
which it interacts = Game matrix
Evolutionary Game Theory

What happens at equilibrium?
◦ Nash equilibrium doesn‘t make sense since no
individual is changing their strategy

Evolutionarily stable strategy
◦ A strategy is evolutionarily stable if everyone
uses it, and any small group of invaders with a
different strategy will die off over multiple
generations
Evolutionary Game Theory
Organism 2
Organism 1

Theorem
S
T
S
T
a, a
c, b
b, c
d, d
◦ Strategy S in a two-player, two-strategy
symmetric game is evolutionarily stable when
either (i) a > c or (ii) a = c and b > d

Theorem
◦ If strategy S is evolutionarily stable, then (S,S) is a
Nash equilibrium.
◦ If (S,S) is a strict Nash equilibrium, then strategy S
is evolutionarily stable.
Routing Games
Players (drivers) commuting from some
node s to t
 All players are making private decisions
about what path to drive to minimize
latency

◦ Latency of an edge depends on the number of
drivers

Q: What happens at equilibrium?
◦ Don‘t care how we got into the equilibrium
Routing Games

Braess‘s Paradox
◦ A new road can hurt performance at
equilibrium
Routing Games

Theorem
◦ The average latency of traffic at Nash
equilibrium is at most 2x the latency of
optimal traffic
 Assumes linear latencies on edges („ax+b“)
 A better bound of 1.33x is known.
◦ In other words:
 If people were somehow told what routes they
should drive, the average latency would at most
33% better than when everyone selfishly picks a
route
Auctions
Selling one item, but don‘t know how
much it‘s worth
 Each bidder (player) has her own intrinsic
value for the item.

◦ Willing to purchase it up to this price
◦ Values are independent
Auctions help us discover this value
 Goal: Maximize social welfare

◦ Sell item to the person who wants it the most
 In terms of own value, not the bid!
Auctions

Dutch auctions
◦ Price gradually decreased until somebody offers,
and gets the item at that price

Sealed-bid 1st –price auctions
◦ Person who wrote the highest bid in an envelope
gets it at that price

Properties
◦ Dutch auctions are strategically equivalent to
sealed-bid 1st –price auctions
◦ They are not truthful: underbidding is a dominant
strategy for all players
 E.g. bidding (n-1)/n fraction of your value (in uniform
distribution) when there are n players
◦ They maximize social welfare (i.e. efficiency)
Auctions

English auctions
◦ Price gradually raised until all bidders drop
out, person with last offer given the item

Sealed-bid 2nd –price auctions (Vickrey)
◦ Person with highest bid gets item, but pays
second highest bid

Properties
◦ English auctions are strategically equivalent to
Vickrey auctions
◦ They are truthful: people bid their true value
◦ They maximize social welfare in equilibrium.
Auctions
How much does the seller make?
 1st -price and Dutch auctions:

◦ Bidders reduce their bids by a factor of (n-1)
/n
◦ Expect largest bid to be n / (n+1)
 Expect revenue: (n-1) / (n+1)
 2nd
price and English auctions:
◦ Seller commits to collecting less than max. bid
◦ Look at highest and second-highest bids
 Expect revenue: (n-1) / (n+1)

Known as revenue equivalence
Part IV (and V)

Chapters 13 - 15, 18
Sponsored search
Need to auction multiple ad slots with
different properties
 If we knew buyers‘ valuations, we could
use a matching market

Sponsored search

Theorem
◦ We can always set prices so that if buyers buy
the item they most want, all items are sold
(market clearing prices)
 Done by gradually raising prices until there is a
unique perfect matching between buyers and sellers
◦ Market clearing prices always produce socially
optimal outcome

But we don‘t know the valuations...
◦ Want to encourage truthful bidding
Sponsored search

Google‘s Model: Generalized Second Price
Auction
◦ Slot i is assigned to the ith highest bidder at a
price per click equal to the (i+1)st highest
bidder‘s bid
◦ But this does not encourage truthful bidding!
 However, it may give Google higher revenue even
though this is not well understood
Sponsored search

VCG mechanism
◦ Idea: Each individual is charged the harm they
caused the rest of the world

Theorem
◦ Truthful bidding is a dominant strategy in VCG
for each buyer
◦ The VCG assignment is socially optimal
 It maximizes the total valuation of any perfect
matching of slots and advertisers
Structure of the web

Hyperlinks on the web form a directed graph
◦ In the early days, links were navigational
◦ Now many links are transactional
 E.g. when you click on your shopping basket

Two types of directed graphs
◦ DAG (Directed Acyclic Graph)
 If u can reach v, then v cannot reach u
◦ Strongly connected
 Any node can reach another via a directed path

Theorem
◦ Any directed graph can be expressed in terms of
these two types
Structure of the web

Strongly connected component (SCC)
◦ Every pair of nodes can reach each other
◦ No larger set with this property

Theorem
◦ Every directed graph is a DAG on its SCCs

On the web, there is just one giant SCC
Ranking web pages


How should we sort search results?
Observation
◦ Should do link analysis, i.e. consider links to be
votes

Hubs and authorities (HITS)
◦ Quality as an expert (hub)
 Total sum of votes of pages pointed to
◦ Quality of content (authority)
 Total sum of votes of experts
◦ Principle of repeated improvement
◦ Theorem
 HITS converges to a single stable point
◦ Sort results by decreasing authority score
Ranking web pages


Hubs and authorities depend on the topic
(e.g. „cars“). Expensive to compute.
PageRank
◦ A page is important if it is pointed to by other
important pages
 Again, principle of repeated improvement
◦ On what websites will a random web surfer
spend his time?
◦ PageRank(i) = probability the random web surfer
visits page i at any given time
 Sort result pages by decreasing PageRank
◦ Fact: PageRank is the first eigenvector of the
adjacency matrix
PageRank example
Ranking web pages

Properties of PageRank
◦ Problem: Spider traps and dead ends
 Places where you can‘t get back to the SCC
 Solution: Do random jumps (teleports) to any
page about 15% of the time
◦ Ranking is independent of the topic unlike
HITS
 Could do personalized PageRank by teleporting to
topic-relevant page instead
Power laws

It is not unlikely to find someone who has
double your wealth (or salary)
◦ Far less likely to find someone who‘s double
your height (i.e. normal distribution)

Such distributions are heavy-tailed
◦ E.g. Amazon‘s sales rank
Power laws

Look at the in-degree distribution of the
web
◦ Very many pages have a small degree
◦ But reasonbly many pages with an insane number
of in-links

Power-laws
◦ Bulk of the degree distribution shows up as a line
on log-log scale
◦ The fraction of pages that have k in-links is
approximately 1 / k2, a power-law
◦ More generally:
 if the fraction of (population) that has k (something)
follows 1 / kα then power-law with exponent α
Power laws

Exponent typically between 2 and 3 for
any popularity measure (many examples!)
Power laws


Why do power laws arise?
Preferential attachment model
◦ A.k.a. rich-get-richer model
◦ Idea: People have a tendency to copy decisions of
people who act before them
◦ Web pages arrive sequentially
 With probability p they:
 Link to a previous page uniformly at random
 With probability 1-p they:
 Link to a page with probability proportional to that page‘s indegree
◦ Theorem: This model produces a network with
a power-law degree distribution
 The exponent is 1+1/(1-p)
Part VI

Chapters 16,17, 19 and 20
Information cascades

Herding or information cascade
◦ You join a crowd even if it contradicts your
private information
◦ Could be rational: Others might have more
information than you do!

Cascades may not lead to optimal outcome
◦ First few people could be wrong

Cascades can be based on very little
information
◦ People start ignoring their private information
once the cascade begins

Cascades are not robust
◦ Only a bit more information can break it
 Emperor‘s new clothes
Collective action

Some decisions have direct benefit effects
◦ The „network effect“ – buying a fax machine is
better for all previous fax machine owners

Model where everybody sees all actions
◦ Each person i has a threhold ti
◦ Will adopt behavior if at least ti other people are
adopters
◦ F(x) = fraction of people with threshold ≤ ti

Can simulate the dynamics to find a fixed
point
◦ When F(x) = x, no more changes to adoptions
Network effects
Number of adoptions will
increase to the next fixed point
F(x)
x (number of adopters)
Number of adoptions will
decrease to the next fixed point
Network effects
But model ignores the network
 How should we organize a revolt?

◦ Personal threshold k: I will show up if at least
k people show up.
◦ People know the threshold of their friends

Each person is playing a 2-person game
with each of his friends
◦ Great if both choose same option
◦ Payoff: a if both choose A, b if both choose B
◦ Fact: Node will choose A if b/(a+b) fraction
of friends choose A
Network effects

Theorem
◦ The use of a new technology A spreads
monotonically (i.e. nobody will switch back)
Small world phenomenon

People are connected by short paths
◦ Milgram experiment: six degrees of separation
◦ MSN experiment: average of 6.7 hops

Surprising because social networks tend
to have high clustering coefficients
◦ This impedes the exponential growth (i.e.
assuming my 100 friends each have a 100
distinct friends, etc.)
Small world models

Suppose all edges are created
independently with probability p
◦ The Erdos-Renyi random graph model

Properties
Small world models

Theorem
◦ The diameter of a random 3-regular graph on
n nodes is O(log n) with high probability.
◦ Gives us a working definition of what „short
paths“ really means: O(log n)

But Erdos-Renyi random graphs don‘t
capture triadic closure as in social
networks
◦ Modify model to have lots of local edges, and
a few random links
Small world models

Watts-Strogatz model
◦ Goal: Create a random graph with lots of
triangles (local edges) and small diameter
◦ Start with a regular lattice (e.g. ring or a grid)
◦ Rewire:
 For each edge with prob. p, move the other end to
a random vertex
◦ Theorem
 Diameter is O(log n)
Small world networks
Small world networks

Observation: People are able to find
these short paths in a decentralized way!
◦ Geographic navigation should work in polylogarithmic time
 Geographic navigation: Forward the message to
the node I know closest to the destination
◦ Not captured by the Watts-Strogatz model
 Theorem: Takes at least n2/3 steps to reach target

Need a model such that geographic
navigation algorithm works naturally
Small world networks

Kleinberg‘s model
◦ The random edges in the Watts-Strogatz model
were too random for navigation

Create long-range edges with probability
◦ P[u links to v] = 1 / distance(u,v)α
 distance(u,v) is the grid distance between u and v
◦ High α: Mostly know people close by
◦ Low α: Know people from very far away

Theorem
◦ For α=2, geographic navigation takes O(log2 n)
time
◦ For α≠2, no algorithm can navigate in poly-log
time
 Actually it‘s α=dimension of the grid
Small world networks

Kleinberg‘s model fits real-world data well
◦ Using ZIP-codes in LiveJournal and measuring
distance by rank (# people living closer), get
an excellent fit to 1 / rank(u,v)

Implications for peer-to-peer networks
◦ Can construct networks that allow for
efficient search for content
 Chord from MIT is the best known
◦ Efficient: find a file using O(log n) messages

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